Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Large Potential for CH<sub>4</sub> Mitigation and Yield Improvement in China's Paddies Through Locally Optimized N Management.

Global change biology·2026
Same author

Correction: NIDD-enabled lightweight intrusion detection for effective DDoS mitigation in 5G and beyond.

Scientific reports·2026
Same author

Real-Time Named Entity Recognition from Textual Electronic Clinical Records in Cancer Therapy Using Low-Latency Neural Networks.

Big data·2026
Same author

Neurosymbolic Digital Twin for Cardiovascular Disease Prediction and Personalized Modeling.

IEEE journal of biomedical and health informatics·2025
Same author

Hybrid quantum neural network models for fruit quality assessment.

PloS one·2025
Same author

Towards Clinically Applicable Large-Model-Based Privacy-Preserving Polyp Segmentation: A Federated LoRA Approach to Colonoscopy.

IEEE journal of biomedical and health informatics·2025
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Jan 10, 2026

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

651

全景图像驱动的点云初始化用于3D重建

Haoyu Qian1,2, Lidong Yang1,2, Jing Wang3

  • 1School of Digital and Intelligent Industry, Inner Mongolia University of Science and Technology, Baotou 014010, China.

Sensors (Basel, Switzerland)
|November 27, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种新的以文本为导向的3D场景生成管道,使用全景图像和3D高斯斯点击. 它克服了改善虚拟现实和数字双胞胎应用程序的初始化挑战.

关键词:
3D重建重建的3D重建全景图像 全景图像点云初始化 点云初始化

更多相关视频

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
09:19

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

1.4K
Rapid Acquisition of 3D Images Using High-resolution Episcopic Microscopy
07:27

Rapid Acquisition of 3D Images Using High-resolution Episcopic Microscopy

Published on: November 21, 2016

8.0K

相关实验视频

Last Updated: Jan 10, 2026

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

651
Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
09:19

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

1.4K
Rapid Acquisition of 3D Images Using High-resolution Episcopic Microscopy
07:27

Rapid Acquisition of 3D Images Using High-resolution Episcopic Microscopy

Published on: November 21, 2016

8.0K

科学领域:

  • 计算机视觉 计算机视觉
  • 计算机图形 计算机图形
  • 人工智能的人工智能

背景情况:

  • 微分染具有先进的静态3D场景重建.
  • 现有的方法通常依赖于密集的多视图图像,这是很难获得的.
  • 不够的初始化仍然是3D场景重建的一个重大挑战.

研究的目的:

  • 提出一个基于文本的3D场景生成管道,解决初始化问题.
  • 提高3D场景重建的质量和效率.
  • 为了减少对密集多视图图像的依赖.

主要方法:

  • 使用全景图像作为中间表示.
  • 整合了3D高斯斯裂纹,以改善重建.
  • 实现用于点云初始化的斐波那契格子采样.
  • 采用密集的视角伪标签策略,用于教师-学生蒸.

主要成果:

  • 实现了更准确的场景几何和强大的特征学习.
  • 与最先进的方法相比,证明了卓越的性能.
  • 在没有明确的多视图地面真相的情况下,通过标准重建指标验证的有效性.

结论:

  • 拟议的管道有效地解决了3D场景重建中的初始化挑战.
  • 该方法为基于文本的3D场景生成提供了一个有前途的方法.
  • 这项工作通过提高重建能力来推进虚拟现实和数字双胞胎技术.